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dc.contributor.authorMoon, Todd K.
dc.contributor.authorPeel, Christian B.
dc.contributor.authorBudge, Scott
dc.date.accessioned2016-04-29T21:52:00Zen
dc.date.available2016-04-29T21:52:00Zen
dc.date.issued2001-10en
dc.identifier.issn0884-5123en
dc.identifier.issn0074-9079en
dc.identifier.urihttp://hdl.handle.net/10150/607570en
dc.descriptionInternational Telemetering Conference Proceedings / October 22-25, 2001 / Riviera Hotel and Convention Center, Las Vegas, Nevadaen_US
dc.description.abstractVery fast tree-structured vector quantization employs scalar quantization decisions at each level, but chooses the dimension on which to quantize based on the coordinate direction of maximum variance. Because the quantization is scalar, searches are no more complex than scalar quantization - providing significant improvement in complexity over full-searched or even tree-structured vector quantization - but the method preserves the shape and memory advantages of conventional vector quantization. However, the space filling advantage of VQ is forfeited, since each Voronoi cell is a rectangular cuboid.
dc.description.sponsorshipInternational Foundation for Telemeteringen
dc.language.isoen_USen
dc.publisherInternational Foundation for Telemeteringen
dc.relation.urlhttp://www.telemetry.org/en
dc.rightsCopyright © International Foundation for Telemeteringen
dc.titleVERY FAST TREE-STRUCTURED VECTOR QUANTIZATIONen_US
dc.typetexten
dc.typeProceedingsen
dc.contributor.departmentUtah State Universityen
dc.contributor.departmentBrigham Young University, Provoen
dc.identifier.journalInternational Telemetering Conference Proceedingsen
dc.description.collectioninformationProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.en
refterms.dateFOA2018-06-28T04:19:44Z
html.description.abstractVery fast tree-structured vector quantization employs scalar quantization decisions at each level, but chooses the dimension on which to quantize based on the coordinate direction of maximum variance. Because the quantization is scalar, searches are no more complex than scalar quantization - providing significant improvement in complexity over full-searched or even tree-structured vector quantization - but the method preserves the shape and memory advantages of conventional vector quantization. However, the space filling advantage of VQ is forfeited, since each Voronoi cell is a rectangular cuboid.


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